In the field of noisy label learning, errors in labels often lead to uncertainty in features and distortion in the embedding space distribution, resulting in inconsistencies between feature and semantic space, which significantly limit the performance. To address this limitation, we propose a Semantically Embedded Similarity (SES)-based approach. Firstly, SES incorporates prototype-based classifiers alongside traditional linear classifiers to capture class semantics features effectively. Secondly, SES employs a consistent regularization strategy to ensure consistent output distribution of reliable data based on prototypes and linear classifiers. This process aids in learning a certain feature space and aligning it with the semantic space. Finally, SES proposes a mutual constraint strategy to enhance the stability of two classifiers in uncertain spaces by encouraging them to adjust to each other. This strategy leverages their differences to promote collaborative correction of noisy labels, thereby enhancing label accuracy post-correction. Extensive experimentation across multiple benchmark datasets demonstrates SES’s state-of-the-art performance. Notably, our methods both achieved impressive top-1 results under asymmetric noise conditions, significantly outperforming other methods. In addition, SES exhibits promise in real-world noise-label datasets.

To assist with smoking cessation, wearable devices are used to detect the puff (hand-to-mouth gesture) recognition within the smoking activity in a ubiquitous manner. There is a strong assumption that smoking and wearing a smartwatch are usually with the same hand. It will certainly fail to detect smoking gesture with the opposite hand. In this work, we find an interesting phenomenon: smoking can cause a unique pattern of heart rate (HR) which is quite different from other daily activities’ effects. Based on this psychophysiological response, we propose HeartIt, a just-in-time smoking detection solution through measuring the HR by a smartwatch. HeartIt works well for the smoker wearing a smartwatch on either wrist. It can accurately distinguish smoking from other similar hand-to-mouth gestures (e.g., eating, drinking). Moreover, we design an adaptive tracker to trigger the HR sensor once the gesture of lighting a cigarette is detected by low-cost accelerometers. It is robust for different people in various postures and scenarios. Our real-world experiments show that the precision and recall rate of HeartIt reaches 96.7% and 99.8%, respectively.